An accurate and efficient means for estimating/predicting used vehicle market value at the vehicle identification number (VIN) level using historical data has continually been pursued. Especially when large numbers of vehicles are involved, small errors in estimating used vehicle market value could translate large monetary losses.
Conventionally, the value of a used vehicle is estimated by performing database queries on vehicles having similar features, options and content and having known market values. Once the closest used vehicles are located, the estimated market value is determined by finding the average vehicle market value of the closest used vehicles located.
However, this conventional approach fails to deliver accurate estimations of vehicle market value because of its inability to adjust for vehicle differences at the VIN level. Moreover, many times the inconsistency in the criteria for selecting similar used vehicles further skews the market value estimate. Other methods include performing linear regression on historical vehicle data to determine relative market value and sensitivity of vehicle content on overall vehicle market value. However, there is no mechanism to apply to use linear regression to estimate a used vehicle's market value due to procedural difficulties and lack of accuracy when not combined with local neighbor search.
Accordingly, a need exists for a method for valuing used vehicles wherein the method accounts for vehicle variations at the VIN level.